ArXiv

ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment

2026-08-06 · 14:44 UTC ·Abdulkadir Külçe, Alihan Esen, Cağla Fikir... ·1 min read
Authors
Abdulkadir Külçe, Alihan Esen, Cağla Fikir...
Categories
cs.AI, cs.CL
arXiv
https://arxiv.org/abs/2608.06110v1
PDF
https://arxiv.org/pdf/2608.06110v1

Brief

ECHO is a locally-deployable chronic-care conversational assistant using an agentic ReAct chatbot (via LangGraph), 17 clinical tools, and a temporal knowledge graph for cross-session memory; it reports a 94.9% tool-execution pass rate on 59 scenarios. Its two-stage safety stack (rule-based <1 ms + signed GNN: 88.8% accuracy, 90.6% unsafe recall on 2,537 Turkish queries) and a Whisper+BERT speech module (mean macro F1 0.652) run locally as a web app with no external data transfer. Based on the abstract.

Why it matters

ECHO integrates an agentic ReAct chatbot (orchestrated via LangGraph), 17 clinical tools, and a temporal knowledge graph for persistent cross-session memory; it achieved a 94.9% tool-execution pass rate across a 59-scenario benchmark using GPT-5 Mini.

Key details

  • A two-stage hybrid safety layer intercepts queries with a rule-based tier (<1 ms) for explicit crisis/jailbreaks and a signed GNN with APPNP-style propagation for boundary cases, reaching 88.8% accuracy and 90.6% unsafe recall on a 2,537-query annotated Turkish health dataset, outperforming zero-shot LLM baselines including Llama 3.3 70B.
  • A multimodal speech assessment module (Whisper acoustic encoding + BERT text encoding with cross-attention) estimates emotion, depression, and pain with mean macro F1 = 0.652; the full system is a web app that runs locally on consumer hardware with no external data transfer (GDPR/KVKK compliant).
Source evidence

Abstract

This paper presents ECHO (Enhanced Care &amp; Health Observer), a locally-deployable conversational health assistant for long-term chronic care management. ECHO integrates three complementary software modules developed under shared supervision as a unified system. The core module is an agentic chatbot built on a ReAct loop orchestrated via LangGraph, equipped with 17 clinical tools and a temporal knowledge graph for persistent cross-session memory; it achieves a 94.9\% tool-execution pass rate across a 59-scenario benchmark with GPT-5 Mini. A two-stage hybrid safety layer intercepts all incoming queries: a rule-based layer handles explicit crisis signals and jailbreak attempts in under 1ms, while a signed graph neural network (GNN) with APPNP-style propagation classifies boundary cases by clinical intent, achieving 88.8\% accuracy and 90.6\% unsafe recall on a 2,537-query annotated Turkish health dataset while outperforming zero-shot LLM baselines including Llama 3.3 70B. A multimodal speech assessment module combining Whisper acoustic encoding and BERT text encoding with cross-attention fusion estimates emotion, depression, and pain, reaching a mean macro F1 of 0.652. The full system is implemented as a web application that can run entirely on consumer hardware, with no patient data transmitted to external services, supporting compliance with GDPR and KVKK.

Comment: 5 pages